&lt;title&gt;Recognition and visual feature matching of text region in video for conceptual indexing&lt;/title&gt;

Shoji, K.; Kuwano, Hidetaka; Odaka, Kento

doi:10.1117/12.263425

Cited by 14 publications

(7 citation statements)

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“…Lienhart and Stuber (1996) assume that characters are drawn in high contrast against the background to be extracted and have no actual results for recognition. Kurakake et al (1997) present results for recognition using adaptive thresholding and color segmentation to extract characters. However, with news captions, we observe characters which have pixel values similar to those in the background.…”

Section: Introductionmentioning

confidence: 99%

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Video OCR: indexing digital news libraries by recognition of superimposed captions

et al. 1999

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The automatic extraction and recognition of news captions and annotations can be of great help locating topics of interest in digital news video libraries. To achieve this goal, we present a technique, called Video OCR (Optical Character Reader), which detects, extracts, and reads text areas in digital video data. In this paper, we address problems, describe the method by which Video OCR operates, and suggest applications for its use in digital news archives. To solve two problems of character recognition for videos, low-resolution characters and extremely complex backgrounds, we apply an interpolation filter, multiframe integration and character extraction filters. Character segmentation is performed by a recognition-based segmentation method, and intermediate character recognition results are used to improve the segmentation. We also include a method for locating text areas using text-like properties and the use of a language-based postprocessing technique to increase word recognition rates. The overall recognition results are satisfactory for use in news indexing. Performing Video OCR on news video and combining its results with other video understanding techniques will improve the overall understanding of the news video content.

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Section: Introductionmentioning

confidence: 99%

“…Character recognition in videos to make indices is described by Lienhart and Stuber (1996) and Kurakake et al(1997). Lienhart and Stuber (1996) assume that characters are drawn in high contrast against the background to be extracted and have no actual results for recognition.…”

Section: Introductionmentioning

confidence: 99%

Video OCR: indexing digital news libraries by recognition of superimposed captions

et al. 1999

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“…To construct such systems, both low-level features such as object shape, region intensity, color, texture, motion descriptors, audio measurements, and high-level techniques such as human face detection, speaker identification, and character recognition have been studied for indexing and retrieving image and video information in recent years [3], [4], [10], [11], [13], [19], [21], [24], [27]- [29], [32], [36]. Among these techniques, video caption based methods have attracted particular attention due to the rich content information contained in caption text [1], [2], [6], [9], [11]- [13], [15], [16], [19], [20], [27], [33], [36]. Caption text routinely provides such valuable indexing information as scene locations, speaker names, program introductions, sports scores, special announcements, dates and time.…”

Section: Introductionmentioning

confidence: 99%

“…Even though some address text detection in video frames [1], [5], [11]- [13], [16], [20], [34], they usually treat each video frame as an independent image. When temporal information are utilized, they are used only for text enhancement through multiframe averaging [18] or time-based minimum pixel search [15], [20], [27], [28]. These approaches require text detection and localization for every frame of a video, and careful caption blocks tracing and matching are needed between each frame pair for multiframe enhancement and removal of duplicate captions in different frames.…”

Section: Introductionmentioning

confidence: 99%

A spatial-temporal approach for video caption detection and recognition

Tang

Gao

Liu

et al. 2002

IEEE Trans. Neural Netw.

107

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Abstract-We present a video caption detection and recognition system based on a fuzzy-clustering neural network (FCNN) classifier. Using a novel caption-transition detection scheme we locate both spatial and temporal positions of video captions with high precision and efficiency. Then employing several new character segmentation and binarization techniques, we improve the Chinese video-caption recognition accuracy from 13% to 86% on a set of news video captions. As the first attempt on Chinese video-caption recognition, our experiment results are very encouraging.Index Terms-Chinese caption detection, fuzzy clustering neural networks (FCNNs), video indexing, video OCR, video shot segmentation.

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“…One problem is that when the caption is superimposed on a background image, it is difficult to apply existing OCR (optical character recognition) techniques, since the resolution of the characters is degraded as a result of the small number of scan lines (525 in NTSC broadcasts). Consequently, there have been several studies focusing on the recognition of caption characters [3,9], which have had some success.…”

Section: Analysis Of News Captions With Reference To Suffix Nounsmentioning

confidence: 99%

Compilation of dictionaries for semantic attribute analysis of television news captions

Ide

Hamada

Sakai

et al. 2003

Systems & Computers in Japan

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SUMMARYWith the increase in the amount of video that is broadcast daily, there is an increasing need for storage of video in a systematic way for future reuse and retrieval. In particular, from the viewpoint of importance and usability, it is desirable to index news videos. For adequate automatic indexing based on the text information in the video, it is not sufficient to apply the simple index extraction and annotation methods which have been widely used in conventional methods. It is important to select index candidates with reference to semantic attributes. The purpose of this study is to compile dictionaries which are needed for analyzing the semantic attributes of captions (noun phrases) in TV news videos. We describe the process by which words are extracted from text corpora and a thesaurus for storage on the basis of specified conditions. The quality of the dictionaries is examined by analysis of the semantic attributes of the words appearing in actual news videos, and the results are presented. In evaluation experiments in which an existing proper noun dictionary and temporal noun dictionary were combined and used, a recall of 79 to 93% and a precision of 41 to 71% were obtained. Although the precision is low in this result, it is concluded that the compiled dictionaries are of practical use for indexing since the recall is more important in that case.

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<title>Recognition and visual feature matching of text region in video for conceptual indexing</title>

Cited by 14 publications

References 0 publications

Video OCR: indexing digital news libraries by recognition of superimposed captions

Video OCR: indexing digital news libraries by recognition of superimposed captions

A spatial-temporal approach for video caption detection and recognition

Compilation of dictionaries for semantic attribute analysis of television news captions

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